There s No Such Thing as Gaining a Pound: Reconsidering the Bathroom Scale User Interface

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1 There s No Such Thing as Gaining a Pound: Reconsidering the Bathroom Scale User Interface Matthew Kay, * Dan Morris, * mc schraefel, * Julie A. Kientz *Microsoft Research University of Washington University of Southampton Redmond, WA, USA Seattle, WA, USA Southampton, UK ABSTRACT The weight scale is perhaps the most ubiquitous health sensor of all and is important to many health and lifestyle decisions, but its fundamental interface a single numerical estimate of a person s current weight has remained largely unchanged for 100 years. An opportunity exists to impact public health by re-considering this pervasive interface. Toward that end, we investigated the correspondence between consumers perceptions of weight data and the realities of weight fluctuation. Through an analysis of online product reviews, a journaling study on weight fluctuations, expert interviews, and a large-scale survey of scale users, we found that consumers perception of weight scale behavior is often disconnected from scales capabilities and from clinical relevance, and that accurate understanding of weight fluctuation is associated with greater trust in the scale itself. We propose significant changes to how weight data should be presented and discuss broader implications for the design of other ubiquitous health sensing devices. Author Keywords Weight, scales, health data perception ACM Classification Keywords J.3. Life and medical sciences: Health. INTRODUCTION The bathroom scale is the most ubiquitous tool for diagnosing and managing weight issues arguably, the most ubiquitous health sensor of all and several studies have shown that frequent weigh-ins help maintain weight loss [25,28]. However, people who are watching their weight often have a marked aversion to stepping on the scale [7]. We hypothesize that some of this resistance comes from the design of the scale s interface. Despite its centrality to global health and wellness, the familiar bathroom scale interface has barely changed since it was first introduced about 100 years ago: it still produces a single value representing one s weight at the moment of measurement. Digital displays Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from UbiComp 13, September 8 12, 2013, Zurich, Switzerland. Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM /13/09 $ fully partially not work home other Figure 1. Screenshot of a mobile web app used to collect multiple weigh-ins each day. Participants entered their weight and answered three multiple-choice questions at each weigh-in. The result was added to a running graph of weight over time. have replaced the analog needle, coarse measurements of body fat have been added, and some scales log data for offline review; however, the singular data point is still the main display and is often the only information presented at the time of weigh-in. Most scales answer just one question what do I weigh right now? which may not be the best framing for weight data. We believe there are several issues with current scales that work against an effective understanding of weight management, which we explore in this paper. For example, digital scale readouts convey an unrealistic level of precision, negatively affecting user perception. We also show that many scale users develop a deep, trusting relationship with their scales despite significant misconceptions about accuracy, trends, and fluctuation; in an online survey of over 800 scale users, we found that respondents with less understanding of how weight fluctuates during the day were less likely to trust their scales. This is exacerbated by the fact that the scale interface makes no attempt to inform users about how weight fluctuates. Our work suggests an opportunity to re-imagine the 100-year-old user interface that is still state-of-the-art in weight management, grounded in best practice in weight management research and consumers understanding of weight fluctuation. Further, as scales are part of a larger class of increasingly ubiquitous health feedback devices that provide single-point, instantaneous measurements such as body fat estimators, thermometers, pedometers, and blood pressure cuffs our work provides a foundation for future design in this broader space. yes no

2 The rest of this paper is organized as follows: First, we describe related work in weight management (focusing on scales) and intelligibility of ubiquitous interfaces. Second, we analyze a repository of online reviews of scales, examining consumers understanding of quantitative health measurements in terms of attributes like accuracy, precision, and trends. Third, we outline themes from semistructured interviews with experts in nutrition on the role of the scale in clinical practice and their clients relationships with scales. Fourth, we present results from a study quantifying daily weight fluctuation, which has previously been only anecdotally studied even in the clinical literature. Fifth, we describe a large-scale survey of over 800 participants assessing their understanding of how scales operate, how much their weight typically fluctuates, and their own relationships with scales. Finally, we synthesize design recommendations for weight scales and discuss broader implications for the design of health feedback displays. BACKGROUND AND RELATED WORK Weight Management and Scales As links among obesity, mortality, and other health conditions have become clear [2,9], weight management has become a key part of health practice. Obesity is clinically defined in terms of weight and Body Mass Index (BMI) [4,22]; BMI is itself a function of weight and height. Therefore, the scale plays a central role in diagnosing obesity. The scale is also used as part of the treatment regime for obesity: more frequent use of the scale, such as daily weigh-ins, correlates with better weight maintenance after weight loss [25,28]. Studies have shown people who maintain weight best after weight loss interventions eat healthily, have physical activity in their lives, and regularly monitor their weight [12,27]. Actual approaches to reducing weight are most commonly associated with calorie restriction and increased physical activity [13,16] i.e., having people eat less food than required to maintain their current weight. Finally, the weight scale also allows a patient or clinician to monitor weight fluctuation, which has itself has been directly associated with increased mortality [6,21]. Fluctuation is particularly common in individuals dealing with obesity: numerous studies show that successful weight loss is often followed by a recurrence of obesity, with patients sometimes gaining more than they have lost [6,21]. Because caloric restriction seems to have only short-term benefits and often leads to weight regain, and because weight fluctuation is associated with increased mortality, recent work has asked whether weight management should be based more on healthy behaviors than on instantaneous weight [5,18]. In the consumer space, scales such as the Withings and the Fitbit Aria have adopted a self-tracking approach: these scales automatically upload weight and body composition to a website where users can view graphs of their weight over time. However, despite innovations in offline feedback, the fundamental user interface of the scale at weigh-in remains essentially unchanged, reflecting only instantaneous weight. One exception is a Weight Watchers scale that displays the difference between current weight and a goal weight (or the previous measured weight); however, this still treats single data point measurements as meaningful reflections of current weight and does not inform users of broader patterns of weight fluctuation. Intelligibility of Feedback in Ubiquitous Computing One of the core challenges in scale interfaces we will discuss throughout this paper is users understanding of the underlying data how weight typically fluctuates and the uncertainty associated with measuring it. Lim and Dey have studied the effects of the intelligibility of context-aware systems on user perceptions essentially, how transparent the reasoning or certainty of these systems are to users [14,15]. They found that exposing the certainty of a system for example, a confidence region in location-aware systems improves users perceptions of the accuracy and appropriateness of a system, so long as the certainty is good enough [15]. In general, the effect of displaying uncertainty on task performance seems to vary by application, sometimes having positive [1] or negative [20] effects. Other work has looked at using natural-language generation to describe inferences in health data [17,23] as a way to improve human inference. We believe this approach may be promising for weight data, and a systematic understanding of people s grasp of statistical vocabulary is essential to it. Researchers have tried to quantify words of estimative probability by having people assign numerical probabilities to words like likely, uncertain, impossible, and so on [11]. Similarly, confusion around measurement descriptions such as precision and accuracy has been explored in science education [24] and in specific scientific domains [26], but we are not aware of similar investigations of lay understanding of such words, despite their frequent use in product descriptions and consumer reviews. ONLINE REVIEWS STUDY We began our investigation into users perceptions of weight scale data with a qualitative analysis of online product reviews from a popular shopping site (amazon.com) for several consumer scales. This study aimed to answer three questions: 1) What are consumers expectations for accuracy in scales? 2) How do these expectations relate to consumers satisfaction with devices? and 3) What terminology do consumers use to express these expectations? We analyzed product reviews for four popular scales: the Withings scale, the Fitbit Aria, a Tanita scale, and a Weight Watchers scale. Amazon.com reviews include two pieces of metadata: a 5-point product rating and a yes-or-no helpfulness rating (derived from the question was this review helpful to you? ). The helpfulness rating overestimates the helpfulness of reviews with a small number of positive reviews, so we convert it to a helpfulness score by taking the lower bound of its 95% binomial confidence interval.

3 From a corpus of 1084 reviews, we selected those with at least one helpfulness rating (855 reviews). Of these, we considered only 1-, 2-, 4-, and 5-star reviews (817 reviews) and then coded 100 reviews (the top 50 with 1 or 2 stars and the top 50 with 4 or 5 stars, ordered by helpfulness score). We used affinity diagramming to identify recurrent themes within this subset around users understanding of precision, accuracy, and uncertainty. We derived a coding scheme from these themes with 44 codes across 5 categories: motivations for using the device, how reviewers test accuracy/reliability, consistency expectations, factors discussed with respect to data quality, and interpretations of noisy data. The reviews were coded, and we used frequency profiling [19] to identify codes that were more frequently found in 4- or 5-star reviews (positive reviews) than 1- or 2- star reviews (negative reviews), and vice versa. Results Trend Focus vs. Data Point Focus Positive reviews were more likely to exhibit a trend focus (28% of positive reviews, 4% of negative reviews). Rather than discussing problems with individual readings, reviewers discussed the overall value of the scale in surfacing fitness trends. For example, from a positive review: However, body weight fluctuates throughout the day and week. With this scale, I've found myself weighing myself several times per day and looking at my data over a week or month, clear trend lines can be seen despite the daily fluctuations. Ultimately, this is the reason that I bought the scale and makes me very happy. This reviewer accepts fluctuations in the data, reasoning that the overall trend is more important. In contrast, negative reviews were more likely to quantify the perceived precision of a device and then express a desire for more consistent readings (2% of positive reviews, 26% of negative reviews), either within the device or as compared to other devices; for example (from a negative review): The weight ranges +/- 1.5 lbs each time you use it. So let's say you weight [sic] 150 on the scale at your doctor's office. you can expect your reading to be anywhere between to when using this scale. [ ] I can't rationalize keeping a $150+ scale that just isn't accurate. Consumers expectations for the accuracy and reliability of scales seem to vary depending on their model of use. Those with an understanding of or a focus on trends seem more willing to tolerate noisy data, so long as they can establish a baseline from which to observe change. By contrast, those who gave negative reviews were more likely to focus on perceived noise in the data, even if the magnitude of that noise was similar to that reported in positive reviews. Vocabulary and Terminology In total, 68 of the 100 reviews we coded discussed issues around accuracy, precision, or uncertainty. To get a sense of the vocabulary used to express these concepts, we counted the number of reviews containing various words and their derivatives (we list words here only by one form, e.g. consistency for consistent/consistency and derivatives). By far the most-used term was accuracy (in 48/68 reviews), followed by consistency (22/68), fluctuation (10/68), variance (8/68), precision (6/68), reliable (5/68), and repeatable (3/68). We note that even in this small sample, words were not used consistently by reviewers: for example, precision was used to refer both to the concept of accuracy and of precision by different reviewers. We also observed a strong preference for the use of the term accuracy to refer broadly to issues of measurement uncertainty. We therefore believe that a more systematic investigation of vocabulary for expressing uncertainty is warranted. EXPERT INTERVIEWS We interviewed four experts on weight change to validate the findings from our online review study, to better understand how scales are used in weight management, and to learn how experts see the effects of scale use on their users: E1, a professional strength and nutrition coach, works with clients trying to lose weight and clients trying to add muscle mass for specific athletic activities. E2, a dietician whose practice includes both athletes and non-athletes dealing with body weight issues. She is also an author of two cookbooks on healthy eating. E3, an osteopathic physician who works in a family medical practice and focuses on weight loss issues. He works in a low-income area with high rates of obesity. E4, the author of popular books and a blog on nutrition practices and a practicing fitness and nutrition coach. He primarily works with clients looking to lose weight. We conducted a semi-structured interview with each expert, focusing on their background, perceptions of scales, how scales fit into their practice, and their clients perceptions of weight and scales. We used affinity diagramming of transcripts to identify high-level themes, discussed below. Results Scales Can Reinforce Inappropriate Goals. E1 and E2 both stressed that while weight is important, it is not always a complete picture of clients progress toward fitness goals. E1 noted that many people do not make the connection that body composition is often more important than weight and that there s people that completely change their body composition and stay the same weight. E2 also noted that people use weight as an inappropriate goal. One of her clients was hung up because she couldn t get to 125 lbs, even though in photos she clearly had a lean body composition. E2 stated that a specific weight as a number is often such an identity for people, and that people are not so obsessed with your shoe size. E4 called these assumed numbers: a lot of people decide on a number at the beginning that they think they will look good

4 at. These issues were reflected in how E1, E2, and E4 use weight with clients: as one measure amongst several, including body fat calipers (E1 and E2) and circumference measures (E1, E2, and E4), e.g. waist or shoulder circumference. E4 noted, weight is an excellent tool when used in combination with other metrics. Emotional Connection E2 s observation that weight can act as an identity for people reflects a broader theme of emotional connections to scales and weight that pervaded our discussions with experts. E1, E2, and E3 discussed how they must tailor their recommendations to clients, depending on how comfortable they estimate each client will be with regular weighing. E1 noted that weighing daily would drive most people batty ; they have an emotional experience they see numbers and it s not what they expect ; and that weight can move wildly for some clients; e.g., simply by changing the proportion of carbohydrates in one s diet, a person might see a change of 5 8 lbs. E1 described one client: There was a fellow that was ignoring the other measures [he only looked at weight] He was trying to lose weight, and he gained a pound. He was blaming external forces, he was venting: This isn t working! I pointed out, Well, you lost a few inches off your waistline. It was a very emotional reaction from a level-headed guy. Overreaction to Fluctuations E2 noted that people react out of proportion to small changes in weight of 1 2 lbs and they extrapolate forward in their minds. She described clients as getting the horrors when they feel like their weight moves in an undesirable direction. E4 noted people can get kind of crazy and tend to think of small weight changes as absolute instead of transient. He has to tell them: let s wait a day or two and see what happened. He also noted a tendency for some people to weigh themselves at home and the gym and worry about differences of a pound or two without considering differences in the scales used. E1 and E3 both tailored their recommendations to their estimation of a patient s ability to handle regular weighing; as E3 noted: some people get bent out of shape if they weigh themselves every day. Regular Weighing Still Has Significant Value Despite the potential issues with weighing our experts outlined, all of them considered it an important practice and recommended most clients weigh themselves about once a week. Recognizing the tendency for weight to fluctuate during the day from their own experience, they suggest clients weigh in at a consistent time of day and under similar conditions (e.g., just before breakfast) and typically once a week (E1 estimated daily fluctuations at 3 5 lbs, and E4 at 3 4 lbs, though neither were aware of studies measuring this fluctuation). At the same time, E1 noted the potential value of weighing more often: if they can mentally take it, I tell them to go every day: you can see amazing trends. He even described some clients who weigh multiple times a day: They really start to connect to how certain behaviors and food choices affect data, but noted that while some people get excited by connecting data to behaviors or conducting self-experiments, there is a personality split: this sort of tracking works more for people who have a bias towards data, a split also noted by our other experts. Finally, E4 stated, the place where I like it [the scale] is, after getting to a good point, understanding what a healthy weight range is. He described scales as particularly valuable for supporting weight maintenance among people who have lost weight: once people get to a steady weight and establish a healthy weight range, they can see when weight gets to an amount outside of a comfortable zone then adjust their behavior. In general, our experts cast the best use of weight as an indicator of a trend rather than as an absolute value; as E4 said: We only really want to know: would that line be kind of going down or kind of going up. Education and Rationale are Essential E2 and E3 both emphasized the importance of educating clients to help them understand weight changes. E3 noted that a third to a half of a visit typically consists of providing background information for example, if a client gains a couple of pounds, E3 has to explain that it is probably water. E2 echoed this sentiment when talking about client compliance: Mandates don t work. When you explain why, you get better compliance. All experts discussed the need to explain potential sources of weight fluctuation to clients as a way to allay their concerns about small changes in weight. These practices suggest that perhaps approaches to conveying intelligibility particularly rationales or explanations of why data looks as it does [14] may have strong impact in the weight space. WEIGHT TRACKING STUDY The results of the online reviews study and our expert interviews support our hypothesis that a significant number of consumers have misperceptions about scale accuracy and weight fluctuation. However, we cannot accurately assess people s understanding of daily weight fluctuation without some standard against which to judge their perceptions. We were unable to find studies of within-day weight fluctuation in the literature (weight change is typically studied between days). Furthermore, consultations with physicians and dieticians suggested such data could help them allay clients concerns, but they were not aware of any studies that had collected it. To begin to fill this gap in the literature, we devised a study to gather data on within-day weight fluctuation. We specifically sought to answer two questions: 1) How much does a person s weight typically vary during a single day? and 2) How much do weighing conditions like clothes or the scale used affect weight measurements? Both of these questions inform our hypotheses that single-point, context-free measurements overlook important aspects of weight management and that consumers place undue emphasis on numerical precision in weight measurements.

5 Component Effect (lbs) SD Clothing F 2,641 = p <.0001 partially t 641 = 2.81 p <.01 fully t 641 = 7.71 p <.0001 Table 1. Effects of weighing conditions on weight. We used a journaling approach to collect multiple weigh-ins from users on a mobile web app (Figure 1). We recruited within our institution (via a departmental list) and on weight-related Internet forums. For participants within our institution, we placed 10 digital weight scales of the same model throughout our building in easily accessible areas: kitchenettes, locker rooms, and the building foyer. Participants were not compensated but were presented with graphs of their own data as an enticement for the curious (Figure 1). We asked participants to weigh themselves at least 3 times daily for a period of at least 10 days, spanning two weekends, and to use our web app to report their weight immediately after weighing. In addition to the user s current weight, our phone app requested clothing state ( fully, partially, or not ), scale ( work, home, or other ), and phone presence during weighing ( present or not present ). Time of entry was logged automatically. After excluding participants that provided three or fewer readings, we had data from 23 participants (69% male): 17 internal to our organization and 6 external. Participants weighed themselves an average of 28.8 times (sd=23.8, min=6, max=109); 15 participants gave us at least 20 measurements. Mean weight among participants was lbs (sd=8.5), mean age was 32.5 (sd=9.4). Results Effects of Weigh-in Conditions Understanding the effects of weigh-in conditions (clothes, scale, etc.) would allow us to better explain potential causes of weight fluctuation to users. We used a mixed-model regression and analysis of variance to analyze the effects of clothing and scale on weight. Clothing was modeled as a fixed effect, allowing us to estimate the average effect of wearing clothes across all participants. Participant and scale (nested within participant) were modeled as random effects, allowing our model to account for the effect of each person s scale separately. Before running this model, the effect of phone presence was accounted for by subtracting the mean weight of a smartphone 0.29 lbs (sd=0.05) taken from a database (http://smartphones.findthebest.com) of 464 models of smartphone. The effects of model components are summarized in Table 1: on average, being partially clothed increased weight by 0.85 lbs and being fully clothed increased weight by 2.17 lbs. Our model also estimates an offset for each scale from the correct weight. The offset range was 4.56 lbs (IQR=1.33 lbs). This is fairly consistent with previous work that found digital scales in a hospital had a range around the standard weight of 5.51 lbs (IQR=1.15) [8], supporting our model s validity. Within-day range Mean (lbs) SD Min Max unadjusted adjusted Table 2. Unadjusted and adjusted within-day weight ranges. Within-Day Weight Variation To estimate typical within-day weight variation, we considered all instances of any participant submitting at least 3 weigh-ins in a calendar day. We then calculated the difference between the maximum and minimum recorded weight for each day; we call this the within-day range. Our model of clothing and scale effects also allows us to also derive an adjusted weight for each weight. We do this by subtracting the effect of the participant s recorded clothing level, scale used, and phone presence from each weight. Using these adjusted weights, we can calculate an adjusted within-day range. While this adjusted range should more closely approximate actual weight fluctuation, the unadjusted range reflects what a scale user is more likely to observe in practice. Therefore, we report both (Table 2, Figure 2a). The mean within-day range was 3.60 lbs (2.72 lbs adjusted), validating our experts estimates of about 3 5 lbs. These results suggest body weight can fluctuate substantially throughout the day. On top of that, changing clothes or weighing on a different scale may have a significant effect on the weight shown on a scale, even if body weight has not changed. Given that product reviews from our first study suggest even changes of a single pound may be important to users, these results indicate that daily observed weight variation could cause undue concern amongst people who weigh themselves often (or with different scales) but who do not fully understand these sources of weight change. Weight Range by Mean Weight We also hypothesized that heavier individuals might see a greater within-day weight fluctuation, implying that it would be better to examine within-day weight fluctuation as a percentage of each individual s mean weight. Somewhat to our surprise, we found no evidence of a correlation between an individual s mean weight and their mean withinday weight range (F 1,19 =0.0001, R 2 =-0.05, p=0.99). While we saw no evidence for such a relationship, we note that we had no participants with a mean weight over 300 lbs. It is possible that in those with very high (or low) weight, fluctuation patterns differ from those observed in our sample. Focus & Limitations We stress that the regression used in the study was only to approximate the fluctuation in weight measurements, as our primary focus is on examining the appropriateness of instantaneous measurements of weight from an end-user perspective. That is, the physiological influences on weight fluctuation (menstrual cycle, salt intake, etc.) are not in our scope: we wanted to know what people s weight fluctuations look like to them, regardless of what caused them. Our

6 focus only on fluctuation not on causes of fluctuation precisely complements our observation that scales do not use or present any of this contextual information either. SCALE PERCEPTIONS SURVEY A pervasive theme throughout our investigation was users struggle to understand and account for fluctuations in data: both in product reviews and expert interviews, we encountered mismatches between the magnitude of reactions to weight change and the actual significance of that change, given our knowledge of weight fluctuation derived from our weight tracking study. We conducted an online survey to better gauge the relationship between scale users perceptions of weight data and their understanding of weight fluctuations e.g., do people with a better understanding of weight fluctuation trust their scales more? Noting the inconsistent use of statistical vocabulary by product reviewers, we were also interested in establishing a common lay vocabulary for scale properties like accuracy and reliability. We recruited via mailing lists within our organization, on weight- and fitness-related forums, and on Twitter. Internal participants were offered a $10 gift card; external participants were entered into a raffle for a $50 gift card. We also invited participants in our weight tracking study to complete an exit survey that included about that study as well as all questions from the scale perceptions survey. These participants were offered the same compensation as surveyonly participants for completing the exit survey. Results Of 892 total respondents, 18 had were participants in our weight tracking study and 30 were internal to our institution. Of the 861 others, 716 were recruited via E4, who advertised our survey to his mailing list. 59% were male and 79% weighed themselves regularly. 67% reported they were trying to lose weight, 15% to maintain weight, 5% to gain weight, and 9% had other goals (e.g., changing fat/muscle composition). The next three subsections address respondents understanding of weight fluctuation, the connection between that understanding and their perceptions of scales, and common vocabulary for scale accuracy and reliability. Understanding of Within-Day Weight Fluctuation To estimate respondents understanding of typical daily weight fluctuation, we prompted them with the following: Imagine your heaviest weight on a typical day and your lightest weight on the same day. Please indicate how likely you think each of the following scenarios is. Respondents then indicated whether they thought each of the following scenarios was very likely, somewhat likely, somewhat unlikely, or very unlikely: Your heaviest weight is more than 10 lbs (4.5 kg) higher than your lightest weight. Your heaviest weight is 8 lbs (3.6 kg) higher than your lightest weight. (This question was repeated for 6 lbs, 4 lbs, and 2 lbs.) Your heaviest and your lightest weight are the same. In essence, we wanted respondents to indicate their expected distribution of within-day weight ranges. Results of these questions are shown in Figure 2b alongside the distribution of within-day ranges from our weight tracking study. Respondents estimations of within-day weight range were generally good: the shape of their average estimated distribution is similar to our observed distribution. Respondents tended to place 2 lbs or 4 lbs as the most likely weight range, close to our observed 3.6 lbs (2.72 lbs adjusted). However, many still over-estimated both the chances that no weight difference would be observed or the chance that a much larger difference (e.g. 8 or 10 lbs) would be observed. Weight Fluctuation Knowledge and Weight Data Perception To compare responses between respondents who had a more or less accurate understanding of daily weight fluctuation, we categorized their likeliness estimates into accurate and inaccurate estimates. Accurate estimates were those that: (1) rated the 0, 8, and 10 lbs ranges as Very or Somewhat unlikely, and (2) rated the 2 and 4 lbs ranges as Very or Somewhat likely. We did not factor the 6 lbs range into our categorization. Given this categorization, 326 respondents (36.5%) had inaccurate estimates and 566 (63.5%) had accurate estimates, suggesting that a majority had a good understanding of typical within-day weight fluctuation. While this may not be surprising in a population where most people weigh regularly (and we do not claim that this generalizes broadly), it is noteworthy that even in this population 36.5% of people had inaccurate estimates of weight fluctuation. To investigate the effect of this knowledge on perceptions of weight data, we asked respondents four Likert-scale questions on their attitudes toward scales: unreliability, trust, worry, and eagerness (Figure 3). To analyze the Likert data, we used the Aligned-Rank Transform (ART), which allows nonparametric testing of multiple factors with an ANOVA. We included range estimate quality (accurate or inaccurate) and weighs regularly (yes or no, self-reported: Do you weigh yourself regularly (for example, once a week or more)? ) and their interaction Unadjusted within-day range (lbs) a. Weight tracking study b. Scale perceptions survey Density Adjusted within-day range (lbs) Density very somewhat unlikely unlikely Estimated likeliness somewhat likely Figure 2. a) From the weight tracking study: histograms of within-day weight ranges (max - min weight within a day) before and after adjustment for weigh-in conditions. b) For comparison, from the scale perceptions survey: respondents estimated likeliness of various within-day weight ranges. Within-day range (lbs) 10 =8 =6 =4 =2 =0

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What will I find in this section of the toolkit? Toolkit Section Introduction to the Toolkit Assessing Local Employer Needs Market Sizing Survey Development Survey Administration Survey Analysis Conducting

The Usability of Electronic Stores based on the Organization of Information and Features CHAN KAH-SING Singapore Polytechnic This paper describes an investigation on how the perceived usability of electronic

1. What does the letter I correspond to in the PICO format? A. Interdisciplinary B. Interference C. Intersession D. Intervention 2. Which step of the evidence-based practice process incorporates clinical

Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about

How Data are Obtained The distinction between observational study and experiment is important in statistics. Observational Study Experiment Observes individuals and measures variables of interest but does

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WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club

Maintaining a healthy weight and preventing excess weight gain in children and adults. Cost effectiveness considerations from a population modelling viewpoint. Introduction The Centre for Public Health

CHAPTER 3 Questionnaire Design THREE DIFFERENT TYPES OF QUESTIONS Structured and semi-structured questionnaires are made up of three different types of questions depending on the type of information which

Topic 4 is about how body weight, physical activity, good personal hygiene and clean surroundings affect our health. It explores what a healthy body size is and how to achieve a healthy weight by keeping

Biology 1 Exercise 1: How to Record and Present Your Data Graphically Using Excel Dr. Chris Paradise, edited by Steven J. Price Introduction In this world of high technology and information overload scientists

Section 2: Ten Tools for Applying Sociology CHAPTER 2.6: DATA COLLECTION METHODS QUICK START: In this chapter, you will learn The basics of data collection methods. To know when to use quantitative and/or

Creating an Effective Mystery Shopping Program Best Practices BEST PRACTICE GUIDE Congratulations! If you are reading this paper, it s likely that you are seriously considering implementing a mystery shop

Writing the Empirical Social Science Research Paper: A Guide for the Perplexed Josh Pasek University of Michigan January 24, 2012 Correspondence about this manuscript should be addressed to Josh Pasek,

Student diaries: using technology to produce alternative forms of feedback NUZ QUADRI University of Hertfordshire PETER BULLEN University of Hertfordshire AMANDA JEFFERIES University of Hertfordshire 214

3 Testing Websites with Users 3 TESTING WEBSITES WITH USERS Better Practice Checklist Practical guides for effective use of new technologies in Government www.agimo.gov.au/checklists version 3, 2004 Introduction

Workshop Discussion Notes: Housing Data & Civil Rights October 30, 2014 Washington, D.C. http://www.datacivilrights.org/ This document was produced based on notes taken during the Housing workshop of the

Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.

Online Reputation in a Connected World Abstract This research examines the expanding role of online reputation in both professional and personal lives. It studies how recruiters and HR professionals use

Top 5 best practices for creating effective dashboards and the 7 mistakes you don t want to make p2 Financial services professionals are buried in data that measure and track: relationships and processes,

Research Highlights LONG-TERM CARE IN AMERICA: AMERICANS OUTLOOK AND PLANNING FOR FUTURE CARE INTRODUCTION In the next 25 years, the U.S. population is expected to include 82 million Americans over the

STATISTICS 8, FINAL EXAM NAME: KEY Seat Number: Last six digits of Student ID#: Circle your Discussion Section: 1 2 3 4 Make sure you have 8 pages. You will be provided with a table as well, as a separate

Stability of School Building Accountability Scores and Gains CSE Technical Report 561 Robert L. Linn CRESST/University of Colorado at Boulder Carolyn Haug University of Colorado at Boulder April 2002 Center

Performing a Community Assessment 37 STEP 5: DETERMINE HOW TO UNDERSTAND THE INFORMATION (ANALYZE DATA) Now that you have collected data, what does it mean? Making sense of this information is arguably

STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

Applying Machine Learning to Stock Market Trading Bryce Taylor Abstract: In an effort to emulate human investors who read publicly available materials in order to make decisions about their investments,

1 in Switzerland. Recruiting is increasingly social and Adecco wants to know how it evolves. An international survey, that involved over 17.000 candidates and 1.502 Human Resources managers between March

Measuring Internationalization at Community Colleges Funded by the Ford Foundation AMERICAN COUNCIL ON EDUCATION The Unifying Voice for Higher Education Center for Institutional and International Initiatives